This mixed-method study explores the acceptance of ChatGPT as a tool for Metacognitive Self-Regulated Learning (MSRL) among academics. Despite the growing attention towards ChatGPT as a metacognitive learning tool, there is a need for a comprehensive understanding of the factors influencing its acceptance in academic settings. Engaging 300 preservice teachers through a ChatGPT-based scenario learning activity and utilizing convenience sampling, this study administered a questionnaire based on the proposed Technology Acceptance Model at UTM University's School of Education. Structural equation modelling was applied to analyze participants' perspectives on ChatGPT, considering factors like MSRL's impact on usage intention. Post-reflection sessions, semi-structured interviews, and record analysis were conducted to gather results. Findings indicate a high acceptance of ChatGPT, significantly influenced by personal competency, social influence, perceived AI usefulness, enjoyment, trust, AI intelligence, positive attitude, and metacognitive self-regulated learning. Interviews and record analysis suggest that academics view ChatGPT positively as an educational tool, seeing it as a solution to challenges in teaching and learning processes. The study highlights ChatGPT's potential to enhance MSRL and holds implications for teacher education and AI integration in educational settings.
Mycotoxins, harmful compounds produced by fungal pathogens, pose a severe threat to food safety and consumer health. Some commonly produced mycotoxins such as aflatoxins, ochratoxin A, fumonisins, trichothecenes, zearalenone, and patulin have serious health implications in humans and animals. Mycotoxin contamination is particularly concerning in regions heavily reliant on staple foods like grains, cereals, and nuts. Preventing mycotoxin contamination is crucial for a sustainable food supply. Chromatographic methods like thin layer chromatography (TLC), gas chromatography (GC), high-performance liquid chromatography (HPLC), and liquid chromatography coupled with a mass spectrometer (LC/MS), are commonly used to detect mycotoxins; however, there is a need for on-site, rapid, and cost-effective detection methods. Currently, enzyme-linked immunosorbent assays (ELISA), lateral flow assays (LFAs), and biosensors are becoming popular analytical tools for rapid detection. Meanwhile, preventing mycotoxin contamination is crucial for food safety and a sustainable food supply. Physical, chemical, and biological approaches have been used to inhibit fungal growth and mycotoxin production. However, new strains resistant to conventional methods have led to the exploration of novel strategies like cold atmospheric plasma (CAP) technology, polyphenols and flavonoids, magnetic materials and nanoparticles, and natural essential oils (NEOs). This paper reviews recent scientific research on mycotoxin toxicity, explores advancements in detecting mycotoxins in various foods, and evaluates the effectiveness of innovative mitigation strategies for controlling and detoxifying mycotoxins.
With the continuous development of technology, traditional marketing methods no longer meet the needs of the main forces of social consumption, and people urgently need more innovative and personalized marketing strategies. E-commerce companies must develop a comprehensive customer-oriented marketing strategy based on big data and multi-channel to achieve their long-term healthy development. This paper first investigated the impact of the digital economy on e-commerce enterprises, focused on the transformation of the digital economy on the marketing model, expounded the development analysis of e-commerce in the digital economy era, and described the development trend of e-commerce marketing in the digital economy era. Then, this paper expounded the current problems faced by e-commerce enterprises, and discussed the lack of integrity, homogeneity, large-scale marketing strategies, and the lack of analysis and application of big data. After that, this paper put forward the marketing strategy of e-commerce enterprises in the digital economy era, and studied it from three aspects, namely, building a reasonable product management structure, marketing strategy based on customized marketing content, and social media marketing strategy based on information sharing. Then this paper proposed to use genetic algorithm to strengthen the marketing strategy of e-commerce enterprises. Finally, based on experiments and surveys, this paper used genetic algorithms to strengthen the construction of e-commerce enterprise marketing strategy in the digital economy, and concluded that the new e-commerce enterprise marketing strategy was 21% more satisfactory than the traditional new e-commerce enterprise marketing strategy. Through comparison, it can see that the integrity of the marketing plan of the new e-commerce enterprise's marketing strategy was 0.33 higher than that of the traditional e-commerce enterprise's marketing strategy, and the integrity of the promotion strategy was 0.34 higher than that of the traditional e-commerce enterprise's marketing strategy. After using the new e-commerce enterprise marketing strategy, the improved management structure was 0.29 higher than that of the traditional monitoring system, and the high quality of products was 0.18 higher than that of the traditional system.
Carbon nanotube (CNT) reinforcement can lead to a new way to enhance the properties of composites by transforming the reinforcement phases into nanoscale fillers. In this study, the buckling response of functionally graded CNT-reinforced composite (FG-CNTRC) sandwich beams was investigated experimentally and analytically. The top and bottom plates of the sandwich beams were composed of carbon fiber laminated composite layers and hard core. The hard core was made of a pultruded glass fiber-reinforced polymer (GFRP) profile. The layers of FG-CNTRC surfaces were reinforced with different proportions of CNT. The reference sample was made of only a pultruded GFRP profile. In the study, the reference sample and four samples with CNT were tested under compression. The largest buckling load difference between the reference sample and the sample with CNT was 37.7%. The difference between the analytical calculation results and experimental results was obtained with an approximation of 0.49%-4.92%. Finally, the buckling, debonding, interlaminar cracks, and fiber breakage were observed in the samples.
Seaweed cultivation has garnered significant interest, driven by its wide range of biomass benefits. However, comprehensive assessments from various perspectives are imperative to ensure the sustainable cultivation of seaweed. Biotic and Abiotic factors can significantly impact seaweed yield in complex commercial farming. Biotic factors include bacteria, fungi, viruses, and other algae, while abiotic factors include environmental conditions such as temperature, salinity, light intensity, and nutrient availability. Additionally, the susceptibility of seaweeds to pests and diseases further compounds the issue, leading to potential crop losses. This study endeavours to shed light on the immense potential of macroalgae cultivation and underscores the pressing need for scientific advancements in this field. The comprehensive review clearly explains the latest developments in seaweed cultivation and highlights significant advances from diverse seaweed research. Moreover, it provides insightful glimpses into possible future developments that could shape the trajectory of this promising industry.
Seaweed research has gained substantial momentum in recent years, attracting the attention of researchers, academic institutions, industries, policymakers, and philanthropists to explore its potential applications and benefits. Despite the growing body of literature, there is a paucity of comprehensive scientometric analyses, highlighting the need for an in-depth investigation. In this study, we utilized CiteSpace to examine the global seaweed research landscape through the Web of Science Core Collection database, assessing publication trends, collaboration patterns, network structures, and co-citation analyses across 48,278 original works published since 1975. Our results demonstrate a diverse and active research community, with a multitude of authors and journals contributing to the advancement of seaweed science. Thematic co-citation cluster analysis identified three primary research areas: "Coral reef," "Solar radiation," and "Mycosporine-like amino acid," emphasizing the multidisciplinary nature of seaweed research. The increasing prominence of "Chemical composition" and "Antioxidant" keywords indicates a burgeoning interest in characterizing the nutritional value and health-promoting properties of seaweed. Timeline co-citation analysis unveils that recent research priorities have emerged around the themes of coral reefs, ocean acidification, and antioxidants, underlining the evolving focus and interdisciplinary approach of the field. Moreover, our analysis highlights the potential of seaweed as a functional food product, poised to contribute significantly to addressing global food security and sustainability challenges. This study underscores the importance of bibliometric analysis in elucidating the global seaweed research landscape and emphasizes the need for sustained knowledge exchange and collaboration to drive the field forward. By revealing key findings and emerging trends, our research offers valuable insights for academics and stakeholders, fostering a more profound understanding of seaweed's potential and informing future research endeavors in this promising domain.
Tetraalkylammonium salt (TAS) is an organic salt widely employed as a precursor, additive or electrolyte in solar cell applications, such as perovskite or dye-sensitized solar cells. Notably, Perovskite solar cells (PSCs) have garnered acclaim for their exceptional efficiency. However, PSCs have been associated with environmental and health concerns due to the presence of lead (Pb) content, the use of hazardous solvents, and the incorporation of TAS in their fabrication processes, which significantly contributes to environmental and human health toxicity. As a response, there is a growing trend towards transitioning to safer and biobased materials in PSC fabrication to address these concerns. However, the potential health hazards associated with TAS necessitate a thorough evaluation, considering the widespread use of this substance. Nevertheless, the overexploitation of TAS could potentially increase the disposal of TAS in the ecosystem, thus, posing a major health risk and severe pollution. Therefore, this review article presents a comprehensive discussion on the in vitro and in vivo toxicity assays of TAS as a potential material in solar energy applications, including cytotoxicity, genotoxicity, in vivo dermal, and systemic toxicity. In addition, this review emphasizes the toxicity of TAS compounds, particularly the linear tetraalkyl chain structures, and summarizes essential findings from past studies as a point of reference for the development of non-toxic and environmentally friendly TAS derivatives in future studies. The effects of the TAS alkyl chain length, polar head and hydrophobicity, cation and anion, and other properties are also included in this review.
This study evaluated the effects of potato, wheat, rice, and corn starch on growth performance, blood parameters, digestive enzyme activity, antioxidative response, and gut microbiota of African catfish, Clarias gariepinus. A control diet (a commercial fish diet) and four different starch (potato, PO; wheat, WH; corn, CO; rice, RC) formulations were fed to African catfish with average weight of 10.5g (n = 30) for eight weeks. The experiment was conducted in triplicates. At the end of the feeding trial, the growth performance of African catfish fed with potato starch (PO) was significantly higher than other treatment groups. Furthermore, this group recorded significant and lowest feed conversion ratio (FCR) compared to other groups. Meanwhile, there were no significant differences in all tested hematological parameters and antioxidative response between the groups. Digestive enzyme activities in the fish intestines, including amylase, lipase, and protease, were significantly higher in African catfish fed with the PO diet. In addition, this group demonstrated substantially lower viscerosomatic index (VSI) and hepatosomatic index (HSI) than other groups, indicating that the fish has more meat on its body. The PO diet group also recorded significantly higher Akkermansia muciniphila, a good gut microbiota. Therefore, the PO diet potentially improves African catfish's growth performance and health status.
This research aimed to identify the factors that influence impulse buying behavior during livestreaming and advance the existing literature based on a proposed conceptual framework grounded in the stimulus-organism-response (S-O-R) model. We also tested the moderating effects of price perception and scarcity persuasion. An online self-administered questionnaire was used to collect data from 837 Chinese participants aged over 18 years. The data were analyzed using partial least squares structural equation modeling using Smart-PLS version 4.0. The findings showed that susceptibility to social influence, impulse buying tendency, cognitive reactions, affective reactions, and the urge to buy impulsively are statistically significant predictors of impulse buying during livestreaming, with price perception and scarcity persuasion as moderators. The study expands the S-O-R model for livestreaming impulse buying in e-commerce context, highlighting its multifaceted nature and revealing the mediating role of Urge to Buy Impulsively in translating cognitive and emotional factors into impulse buying behavior. These insights offer practical guidance for marketers to design tailored strategies that leverage psychological triggers and external cues to enhance consumer engagement and encourage desired behaviors, ultimately leading to more effective marketing campaigns and improved consumer experiences.
The Internet of Vehicles (IoV) emerges as a pivotal extension of the Internet of Things (IoT), specifically geared towards transforming the automotive landscape. In this evolving ecosystem, the demand for a seamless end-to-end system becomes paramount for enhancing operational efficiency and safety. Hence, this study introduces an innovative method for real-time driver identification by integrating cloud computing with deep learning. Utilizing the integrated capabilities of Google Cloud, Thingsboard, and Apache Kafka, the developed solution tailored for IoV technology is adept at managing real-time data collection, processing, prediction, and visualization, with resilience against sensor data anomalies. Also, this research suggests an appropriate method for driver identification by utilizing a combination of Convolutional Neural Networks (CNN) and multi-head self-attention in the proposed approach. The proposed model is validated on two datasets: Security and collected. Moreover, the results show that the proposed model surpassed the previous works by achieving an accuracy and F1 score of 99.95%. Even when challenged with data anomalies, this model maintains a high accuracy of 96.2%. By achieving accurate driver identification results, the proposed end-to-end IoV system can aid in optimizing fleet management, vehicle security, personalized driving experiences, insurance, and risk assessment. This emphasizes its potential for road safety and managing transportation more effectively.
Global warming induces spatially heterogeneous changes in precipitation patterns, highlighting the need to assess these changes at regional scales. This assessment is particularly critical for Afghanistan, where agriculture serves as the primary livelihood for the population. New global climate model (GCM) simulations have recently been released for the recently established shared socioeconomic pathways (SSPs). This requires evaluating projected precipitation changes under these new scenarios and subsequent policy updates. This research employed six GCMs from the CMIP6 to project spatial and temporal precipitation changes across Afghanistan under all SSPs, including SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. The employed GCMs were bias-corrected using the Global Precipitation Climatological Center's (GPCC) monthly gridded precipitation data with a 1.0° spatial resolution. Subsequently, the climate change factor was calculated to assess precipitation changes for both the near future (2020-2059) and the distant future (2060-2099). The bias-corrected projections' multi-model ensemble (MME) revealed increased precipitation across most of Afghanistan for SSPs with higher emissions scenarios. The bias-corrected simulations showed a substantial increase in summer precipitation of around 50%, projected under SSP1-1.9 in the southwestern region, while a decline of over 50% is projected in the northwestern region until 2100. The annual precipitation in the northwest region was projected to increase up to 15% for SSP1-2.6. SSP2-4.5 showed a projected annual precipitation increase of around 20% in the southwestern and certain eastern regions in the far future. Furthermore, a substantial rise of approximately 50% in summer precipitation under SSP3-7.0 is expected in the central and western regions in the far future. However, it is crucial to note that the projected changes exhibit considerable uncertainty among different GCMs.
This study analyses which aspects of sitting and working furniture ergonomics that may be influenced and how they are assessed. To gather information on the types and assessment techniques connected with influencing furniture ergonomics, a systematic review of the literature was conducted. The papers in the systematic review were published between 2012 and 2022. The articles applied the Systematic Reviews and Meta-Analyses (PRISMA) statement guidelines to limit the 41 papers that were eventually included (N = 41) to those containing keywords like ergonomics, human factors, comfort, working furniture, Chair, assessment and evaluation. The research objective of this systematic review is to provide a comprehensive overview of sitting and working furniture and the main findings, obtaining common assessment techniques for this type of furniture and their suitability. According to the relevant studies, the publications were categorized by summarizing factors like region, gender, research methods, ergonomic assessment techniques and methods used, correlation between assessment techniques and methods, etc. Summaries of the data extracted from the included papers are provided and the applicability of some approaches are assessed. Only a small number of authors have evaluated the ergonomics of furniture used in homes. One of the research gaps is the paucity of research on gender segregation, secular trends, and cultural contexts. These studies heavily rely on quantitative research techniques, and the articles may lack credibility due to the homogeneity of the evaluation techniques. Finally, the authors offer some suggestions for the appropriate ergonomic analysis of furniture.
Herbal treatments have been utilized for millennia to cure a variety of ailments. There are over 20, 000 herbal remedies available to treat cancer and other disease in humans. In Ayurveda, traditional plants having revitalizing and nourishing characteristics are known as "Rasayanas." They have anti-inflammatory, anticancer, anti-microbicidal, antiviral, and immunomodulatory effects on the immune system. Immunomodulation is a mechanism through which the body stimulates, suppresses, or boosts the immune system to maintain homeostasis. Plant-derived immunomodulators are typically phytocompounds, including carbohydrates, phenolics, lipids, alkaloids, terpenoids, organosulfur, and nitrogen-containing chemicals. Immunomodulation activity of phytocompounds from traditional plants is primarily mediated through macrophage activation, phagocytosis stimulation, peritoneal macrophage stimulation, lymphoid cell stimulation, and suppression or enhancement of specific and non-specific cellular immune systems via numerous signalling pathways. Despite extensive research, the precise mechanism of immunomodulation of most traditional plants has not yet been fully elucidated, justifying the need for further experimentation. Therefore, this review describes the immunomodulatory agents from traditional plants such as Curcuma longa L., Panax ginseng C.A. Meyer, and Moringa oleifera Lam, further highlighting the common molecular targets and immunomodulatory mechanism involved in eradicating diseases.
The structural and electronic properties of ReS2 different forms - three-dimensional bulk and two-dimensional monolayer - were studied within density functional theory and pseudopotentials. A method for standardizing the description of bulk unit cells and "artificial" slab unit cells for DFT research has been proposed. The preference of this method for studying zone dispersion has been shown. The influence of the vacuum layer thickness on specified special high-symmetry points is discussed. Electron band dispersion in both classical 3D Brillouin zones and transition to 2D Brillouin zones in the proposed two-dimensional approach using the Niggli form of the unit cell was compared. The proposed two-dimensional approach is preferable for low-symmetry layered crystals such as ReS2. It was established that the bulk ReS2 is a direct gap semiconductor (band gap of 1.20 eV), with the direct transition lying in the X point of the first Brillouin zone, and it is in good agreement with published experimental data. The reduction in material dimension from bulk to monolayer was conducted with an increasing band gap up to 1.45 eV, with a moving direct transition towards the Brillouin zone center. The monolayer of ReS2 is a direct-gap semiconductor in a wide range of temperatures, excluding only a narrow range at low temperatures, where it comes as a quasi-direct gap semiconductor. The transition, situated directly in the Γ-point, lies 3.3 meV below the first direct transition located near this point. The electronic density of states of ReS2 in the bulk and monolayer cases of ReS2 were analyzed. The molecular orbitals were built for both types of ReS2 structures as well as the electron difference density maps. For all types of ReS2 structures, an analysis of populations according to Mulliken and Voronoi was carried out. All calculated data is discussed in the context of weak quantum confinement in the 2D case.
Sonar sound datasets are of significant importance in the domains of underwater surveillance and marine research as they enable experts to discern intricate patterns within the depths of the water. Nevertheless, the task of classifying sonar sound datasets continues to pose significant challenges. In this study, we present a novel approach aimed at enhancing the precision and efficacy of sonar sound dataset classification. The integration of deep long-short-term memory (DLSTM) and convolutional neural networks (CNNs) models is employed in order to capitalize on their respective advantages while also utilizing distinctive feature engineering techniques to achieve the most favorable outcomes. Although DLSTM networks have demonstrated effectiveness in tasks involving sequence classification, attaining their optimal performance necessitates careful adjustment of hyperparameters. While traditional methods such as grid and random search are effective, they frequently encounter challenges related to computational inefficiencies. This study aims to investigate the unexplored capabilities of the fuzzy slime mould optimizer (FUZ-SMO) in the context of LSTM hyperparameter tuning, with the objective of addressing the existing research gap in this area. Drawing inspiration from the adaptive behavior exhibited by slime moulds, the FUZ-SMO proposes a novel approach to optimization. The amalgamated model, which combines CNN, LSTM, fuzzy, and SMO, exhibits a notable improvement in classification accuracy, outperforming conventional LSTM architectures by a margin of 2.142%. This model not only demonstrates accelerated convergence milestones but also displays significant resilience against overfitting tendencies.
The construction industry, increasingly prioritizing sustainability, necessitates an exploration of technology and management's role in mitigating material waste at construction sites. This study examines the impact of 3R, IBS, BIM, and MMA in enhancing Construction Site Performance (CSP) in the Malaysian construction sector. Seven hypotheses were formulated to assess the relationship between technology adoption, material management practices, and the moderating influence of Material Management Adoption (MMA) on CSP. Data were collected through an online survey from 295 valid responses in the Malaysian construction sector, focusing on professionals involved in solid waste management. Utilizing Partial Least Squares - Structural Equation Modeling (PLS-SEM) and Statistical Package for the Social Sciences (SPSS), the findings highlight the importance of technological integration, efficient material management, and competitive strategies in effective material waste mitigation. Furthermore, the qualitative aspect of the study, conducted among 6 solid waste organizations in Malaysia, enriches the findings by providing nuanced insights into local practices and challenges. Emphasizing the importance of contextual insights, the study addresses professionals involved in solid waste management within the Malaysian construction industry. The geographical specificity adds depth to the analysis, offering a comprehensive understanding of regional dynamics. Despite acknowledging limitations in technology and material usage, the study offers recommendations for refining waste mitigation and improving construction site performance. This research model offers actionable insights for construction site stakeholders, emphasizing the criticality of waste mitigation and CSP. The results, both quantitative and qualitative, underscore the potential of these practices within the Malaysian construction industry to foster innovation and drive positive change.
This study presents an in-depth exploration of the impact of online learning interactions on student learning outcomes. Drawing from the Stimulus-Organism-Response (SOR) paradigm, our study focuses on the effects of online learning interactions on learners' perception usefulness and ease of use, subsequently impacting their learning outcomes. The study employs a quantitative research methodology, gathering data from a sample of 397 students enrolled in various higher education institutions across China. Data collection involved administering structured questionnaires that were designed to quantitatively assess the three components of the SOR model: stimulus (online learning interactions), organism (students' perceptions), and response (learning outcomes). The measurement model assessment and structural model assessment were conducted. Our findings reveal that online learning interactions can effectively enhance learners' perception of online learning (usefulness and ease of use), thereby influencing their learning outcomes. Notably, perceived usefulness negatively mediates the relationship between online learning interactions and learning outcomes, while perceived ease of use positively mediates this relationship. These findings offer both theoretical and practical implications.
Although there are papers on the persistence of energy series including the persistence of shale gas, the impact of recent developments such as the Covid-19 pandemic and Russia-Ukraine conflict have been rarely explored in the existing literature. This paper examines the structure of shale gas production in the U.S. by looking at the degree of persistence across different areas, with the aim to determine if shocks in the series are permanent or transitory. Using fractional integration methods (which unlike the conventional methods, allow for the determination of the persistence of energy and non-energy series in a robust manner), and different subsamples that include the Covid-19 pandemic and the Russia-Ukraine war, our results indicate that there is a substantial decrease in the integration order in the total shale gas production in the U.S. as well as in four other plays-Haynesville, Permian, Utica and Eagle Ford. However, no differences are observed with respect to the Russia-Ukraine war. There is another group of four series (Marcellus, Niobrara-Codell, Woodford and Rest of US 'shale') with a very small reduction in the degree of persistence and another group of three series with almost no reduction at all in the order of integration (Barnett, Mississippian and Fayetteville). Several implications in terms of policy are reported at the end of the manuscript.
β-carotene is obtained from both plants and animals and has been the subject of intense research because of its provitamin-A, antioxidant, and anticancer effects. Its limited absorption and oxidative degradation significantly reduce its antitumor efficacy when taken orally. In our study, we utilize a central composite design to develop "bio-safe and highly bio-compatible" solid lipid nanoparticles (SLNs) by using only the combination of palmitic acid and poloxamer-407, a block co-polymer as a surfactant. The current research aim to develop and characterize SLNs loaded with β-carotene to improve their bioavailability and therapeutic efficacy. In addition, the improved cytotoxicity of solid lipid nanoparticles loaded with β-carotene was screened in-vitro in human breast cancer cell lines (MCF-7). The nanoparticles exhibits good stability, as indicated by their mean zeta potential of -26.3 ± 1.3 mV. The particles demonstrated high drug loading and entrapment capabilities. The fabricated nanoparticle's prolonged release potential was shown by the in-vitro release kinetics, which showed a first-order release pattern that adhered to the Higuchi model and showed a slow, linear, and steady release over 48 h. Moreover, a diffusion-type release mechanism was used to liberate β-carotene from the nanoparticles. For six months, the nanoparticles also showed a notable degree of physical stability. Lastly, using the MTT assay, the anti-cancer properties of β-carotene-loaded solid lipid nanoparticles were compared with intact β-carotene on MCF-7 cell lines. The cytotoxicity tests have shown that the encapsulation of β-carotene in the lipid bilayers of the optimized formulation does not interfere with the anti-cancer activity of the drug. When compared to standard β-carotene, β-carotene loaded SLNs showed enhanced anticancer efficacy and it is a plausible therapeutic candidate for enhancing the solubility of water-insoluble and degradation-sensitive biotherapeutics like β-carotene.
The (E)-4-((4-hydroxy-3-methoxy-5-nitrobenzylidene) amino) pyrimidin-2(1H)-one (C5NV) was synthesized from cytosine and 5-nitrovanilline by simple straightforward condensation reaction. The structural characteristics of the compound was determined and optimized by WB97XD/cc-pVDZ basis set. The vibrational frequencies were computed and subsequently compared to the experimental frequencies. We investiated the electronic properties of the synthesized compound in gas and solvent phases using the time-dependent density functional theory (TD-DFT) approach, and compared them to experimental values. The fluorescence study showed three different wavelengths indicating the nature of the optical material properties. Frontier molecular orbital (FMO) and molecular electrostatic potential (MEP) analyses were conducted for the title compound, and electron localized functions (ELF) and localized orbital locators (LOL) were used to identify the orbital positions of localized and delocalized atoms. Non-covalent interactions (H-bond interactions) were investigated using reduced density gradients (RDGs). The objective of the study was to determine the physical, chemical, and biological properties of the C5NV. The molecular docking study was conducted between C5NV and 2XNF protein, its lowest binding energy score is -7.92 kcal/mol.